SAP a Leader in Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics

March 9th, 2016

SAP was recognized by Gartner for our continued growth as a Data Warehouse vendor, in particular the phenomenal rise in customers for SAP Business Warehouse powered by SAP HANA. SAP were also rated strongly for our cloud and Hadoop support!

In less than five years SAP have become one of the top five in data warehousing, and are beating Oracle and Microsoft in the ‘vision’ axis. SAP HANA has not only proved to be one of the fastest growing enterprise software products in history, but SAP’s Modern DW vision (more on that in later posts, plus watch for an upcoming white paper) is making traditional data warehouse vendors look very 20th Century!

See the whole Gartner MQ at:


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HANA2016 – SAP Data Warehousing, Today and Tomorrow

On Wednesday (see details below) I’ll be presenting an overview of SAP’s data warehousing capabilities, roadmap and vision.
From a capabilities perspective I’m focusing on some of the key options for HANA – streaming, predictive, text processing, spatial and graph. Lots of good stuff there.
For the roadmap I’m going over the direction we are taking with BW, HANA and S/4HANA.
The fun starts with the vision. I’m going to cover some of the trends in the market, and some of the adoption trends we have uncovered in a survey we commissioned Forrester Research to undertake for us. I’m going to summarize the results of this survey, plus a lot of conversations I’ve had with analysts, our internal experts, customer and partners into my view of where SAP needs to go to become the leader in data warehousing.
Five years ago, a SAP presentation outlining a vision of data warehouse leadership (and by leadership I mean being #1) would have been somewhere on the scale of optimistic to downright delusional, but this has been an incredible five years. Now SAP is in the leadership cluster in both Forrester’s Wave and Gartner’s MQ for data warehousing, and our vision is one of the strongest in the market. It didn’t hurt that we got in-memory right, despite some derision from the greybeards (is Larry’s beard grey yet?). The next step will be even more impactful, and I want to put my thoughts out there and get your feedback. I’ll add any good comments I get to the discussion thread.
Session details:
SAP Insider HANA 2016, Las Vegas, February 16-19, 2016
SAP’s Data Warehousing Solutions: Current Capabilities, Roadmap Plans, and Future Vision
Wednesday 17th February 2016, 10:30am
Margaux 2

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SAP Data Warehousing at TechEd 2015

BW 7.5, Cool Free Sunglasses, and Over the Top Content!

SAP data warehousing promises to be one of the most interesting highlights of this year’s TechEd (Las Vegas, October 2015; Barcelona, November 2015). With over 40 hours of lectures, 60 hours of “Hands On” workshops, 10 Networking sessions, 9 Roadmap sessions and 6 Developer Garages, our data warehousing customers will have to replicate themselves to get all the value on offer. There are sessions covering SAP Business Warehouse; SAP HANA as both a Data Mart and an Enterprise Data Warehouse; the latest and greatest on Dynamic Tiering; some amazing Streaming Analytics sessions; and nobody will want to miss the new Data Warehouse Foundation tool set sessions. But it isn’t all about sessions – there are product announcements, new white papers available, and SAP BW will also take center stage with a completely new demo and reference architecture at the keynotes.

SAP Business Warehouse 7.5 Announcement

Attendees will also get a first look at the upcoming SAP Business Warehouse 7.5 release (and it promises a completely new development model for the first time), including a chance to participate in the ramp up program.

Keynote Demo

Nobody should miss Steve Lucas’ keynote where long-time partner and HANA expert John Appleby will be showing his latest big data warehouse demo – and John has put the pedal to the metal this year – just don’t miss it! Data Warehousing will never be the same again.

SAP HANA Data Warehousing Foundation Tools

If you have not heard about the new tools for SAP HANA to help with partitioning and life cycle management, this is your chance to discover these new, free, tools that became available earlier this year. Check out the sessions and swing by the booth

HANA Data Warehousing Booth (S26)

We have lots of new content waiting for you at the booth as well as experts to talk about SAP’s data warehousing strategy, the latest BW 7.5 announcement, and all the SAP data warehousing products including SAP HANA smart data streaming, SAP HANA dynamic tiering, SAP IQ, and the new Data Warehousing Foundation tools.

We will also have a list of all the data warehousing sessions and customer sessions you can attend!

Also at the booth, we have a limited number of cool sunglasses that not only protect your eyes, but can open bottles as well! These will disappear fast, so make Booth S26 your priority!

Free Personally Signed Copy of “In-memory Revolution”

Bernd Leukert, SAP board member and co-author of the “In-memory Revolution” with Hasso Plattner will be signing copies of his book – get in line early on the show floor.

[Note: Las Vegas Only] Meet Your Peers

There are over 15 customer speakers presenting their experiences, learning and successes on SAP’s data warehousing at TechEd, including Coca Cola, Kimberly Clark, Snapple, Lochkheed Martin and many others. Come to the SAP data warehousing booth for a complete list of all the sessions.

Come and Meet Us at the SAP HANA data warehousing booth!

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TechEd 2015 – Las Vegas – Data Warehousing Customer Sessions

Here is a list of all customer sessions for data warehousing at TechEd 2015 in Las Vegas:
Customer Sessions

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TechEd 2015 – Las Vegas – Data Warehousing Sessions

Here is a link to a list of all date warehousing sessions at TechEd 2015 Las Vegas

All DW Sessions

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Data Warehousing – Go Live

There are two interesting developments which, if they are combined, could bring a whole new meaning to the term ‘Go Live’ when it comes to Data Warehousing projects.

Development #1 – DNA as a Data Store

DNA has the potential to revolutionize data storage. There are several qualities about DNA that make it very appealing:

  • A recent experiment in ‘fossilizing’ DNA promises very long term stability. The research was conducted by Robert Grass and team from ETH Zurich’s Department of Chemistry and Applied Biosciences. “’[We] have revealed how the long-term, error-free storage of information can be achieved, potentially for more than a million years. (
  • The ability to store and retrieve a song written to DNA was described in a May 14th, 2015 episode of BBC’s ‘Inside Science’ (
  • It is estimated that one kilogram of DNA could hold the same amount of data as 14 million Blu-ray disks (14,000,000 x 50GB = 700 PB per kilogram)

Development #2 – Neural Networks

Neural Networks have been making some great strides recently (see this Peter Fingar blog:

Neural networks have fascinated me ever since my pride in being a pretty good backgammon player took a massive setback when I started repeatedly losing to a computer backgammon program named Jellyfish. Jellyfish replicated about the same number of artificial neurons as its namesake – and also developed new tactics and strategies that had eluded humans for thousands of years.

The Big Idea

So what happens when we put these together – can we create a new lifeform that can store data in its own DNA and use its own neural network (i.e. brain) to perform analytics tasks? We all might have a whole new type of ‘mouse’ attached to our computers.


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Information Density is in the Eye of the Beholder

There is a current trend that manifests itself as “there is value in all data, so keep everything, because you don’t know now what is valuable and what isn’t”. This is undoubtedly correct, but is it useful?

Basically, the question is “what is the information density of my data?” Journal entry data is very information dense – a small amount of data indicates when, in what account, and how much real money was transacted as part of business operations, and this data isn’t only information dense, it is frequently used, either at the atomic level, or as part of an aggregation.

The value of other data is not as easily recognizable – is the record of readings every second from a temperature sensor valuable? Or is it only the time and values when there is a change that is of value, and how valuable is that data during the 99.9% of the time that the machine being monitored is running normally?

The inability to be able to measure the information density of a data set used to result in most data that had not been “proven” valuable to be discarded. But in a post Google world, choosing to discard data has become a more uncomfortable decision to make. Google seems to have proven that there is value in just about all data. You might not be interested in the scorer of the goals in the 1950 English FA Cup Final (Reg Lewis of Arsenal scored both, Go Arsenal!), but somebody is.

The Difference between Your Organization and Google
This highlights the difference between Google and most organizations – Google open their data to the world, most organizations limit access of their data to a few hundred data scientists and business analysts, most of who are focused on pertinent business goals, rather than a sudden interest in soccer history. So the value that is realized from the large amounts of data stored by Google, or more specifically the value delivered to Google’s users, is unlikely to be replicated in most organizations because the number of queries, and the scope and range of the queries directed at the data in most organizations is minuscule in comparison.

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Hadoop: the answer to a question not yet understood?

Bernard Marr: “Where Big Data Projects Fail”

Gartner: Top challenge for Hadoop adoption: “Determining how to get value from Hadoop”

Gartner: 68% of Hadoop adoptions driven by C-Suite (management-by-in-flight-magazine alert!)

I love Hadoop, but are we over hyping it? Two questions:
1. If you want cheap storage of large amounts of data, isn’t cloud the answer?
2. Are we overexcited by new technology and trying to find a reason to justify our personal fun?

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Analytics in the 2010′s = Apps in the 1990′s?

Thoughts on the equivalency between the maturity of analytics and the maturity of applications today.

Applications Data Warehouses
1980’s: Build your own 2000’s: Build using RDBMS
1990’s: Buy best of breed and integrate 2010’s: Buy different technologies and integrate
2000’s: Single vendor solutions 2020’s: Single cloud solutions



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